wos-tabfile 0.0.1a1

Extract information from the exported Web of Science's tab delimited text file

WoS Tab File

Extract information from the exported Web of Science's tab delimited text file

Installation

pip install wos-tabfile

Basic Usage

1. Retrieve publication year and numbers of citations in different indicators.

from wostabfile.core import WosTabFile
from collections import OrderedDict

# data source
root_path = "data/social network"
file_path = root_path + "/part1.txt"

# header fields in the text file
# Tag from: https://images.webofknowledge.com/images/help/WOS/hs_wos_fieldtags.html
wos_fields = ["PY", "NR", "TC", "U1", "U2"]
# load data by specific keys
wtf = WosTabFile(file_path=file_path)

table = wtf.generate_table(wos_fields)

print()
# group by year using count for numbers of publications per year
new_table=wtf.group_by(table,key_index=0,value_index=1,method="count")
new_table = OrderedDict(sorted(new_table.items()))

print("Year\tNumber of publication")
for year in new_table:
    print(f'{year}\t{new_table[year]}')

2. Retrieve keywords and its frequency from the bibliometric data

from wostabfile.core import WosTabFile
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize

root_path = "data/social network"
wos_fields = ["DE"]
wtf = WosTabFile(file_path=root_path)
dict_words={}

def singularize(text):
    wnl = WordNetLemmatizer()
    tokens = [token.lower() for token in word_tokenize(text)]
    lemmatized_words = [wnl.lemmatize(token) for token in tokens]
    return (' '.join(lemmatized_words)).strip()

def process_row(rows):
    global dict_words
    for ks in rows:
        wlist=ks[0]
        for w in wlist.split(";"):
            w=w.strip()
            if w=="":
                continue
            w=singularize(w)
            if w not in dict_words.keys():
                dict_words[w]=1
            else:
                dict_words[w]+=1

table = wtf.generate_table_by_folder(wos_fields,func=process_row)
dict_words =  dict(sorted(dict_words.items(),reverse=True, key=lambda item: item[1]))

print("Word\tTerm Count")
for k in list(dict_words.keys())[:20]:
    print(f'{k}\t{dict_words[k]}')
print()
print("Number of unique words: ",len(dict_words.keys()))

Sample bibliometric data in the above examples can be downloaded in this link.

License

The wos-tabfile project is provided by Donghua Chen.